I've a binary classification problem which I want to solve where many features have a lot of missing values.
I know that imputing with mean/median/variance is a solution, but I'd like to run tests only with the original dataset without imputing. XGBoost allows the presence of missing values, while all the scikit-learn algorithms don't (correct me if I'm wrong), even if theoretically algorithm like random forest could accept missing values.
Other than XGBoost, which other python classification algorithms implementations allow the usage of a dataset with missing values?
Best Answer
You can use the h2o package in python, it can deal with missing values.
Random Forest : http://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/drf.html#faq
Gradient Boosting: http://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/gbm-faq/missing_values.html
XGBoost: http://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/xgboost.html#faqs